[PDF][PDF] Fault location in the transmission network using a discrete wavelet transform

M Dashtdar, M Dashtdar - American Journal of Electrical and …, 2019 - researchgate.net
American Journal of Electrical and Computer Engineering, 2019researchgate.net
In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current
signal in order to faultlocation evaluation in network transmission using pre-fault and post-
fault current data of both the terminals of a transmission line. In fact, the basis of the work is
based on the information recorded before the fault at the end of the line and after the fault at
the beginning of the line received by the relay. Obviously, high-frequency components are
created at the time of the fault, which is a way of extracting these components using a …
Abstract
In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to faultlocation evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault). The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.
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